Introduction & Thoughts Behind The Experiment

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2. - We wanted to chose an experiment that was easy to complete, organized, and some what entertaining. - Our original hypothesis, was that people who ate more Candy on a Regular Basis and had a stronger preference for Sweets rather than Chocolate would have more success and be able to correctly identify more skittles in one minute than those who do not. Introduction &Thoughts Behind The Experiment 2

3. 40 Subjects • Objective: • How many Skittles can the Subject identify in one minute based solely on their Taste Buds • Categorical Factors: • Gender • Male or Female • Candy Consumption • Frequent, Sometimes, Never • Candy Preference • Chocolate or Sweet • Hair Color The Task 3

4. Mean: 2.07962 Standard Dev.: 1.45791 Min: 0 Q1: 1 Median: 2 Q3: 3 Max: 6 Overall Quantitative Data The Data is right skewed and unimodal. The center is at the median of 2 skittles with an IQR of 2 skittles as well. The range was (0, 6) skittles. All data points are within the Upper & Lower Fence (UF=6) (LF=-2), thus there are no outliers. 4

5. 1 sample T Interval on the true average # of skittles • Conditions • Statement • Formula • Interval (a, b) • Conclusion

6. Male: Mean- 2.07692 Standard Dev.- 1.45791 Min- 0 Q1- 1 Median- 2 Q3- 3 Max- 4 Female: Mean- 2.10526 Standard Dev.- 1.6632 Min- 0 Q1- 1 Median- 2 Q3- 3 Max- 6 Quantitative Data by Gender The plots of our data broken down by gender shows that the both females and males had the same median at 2 skittles. The range for females was larger, which was (0,6) skittles, compared to the males which was (0,4) skittles. However they both had an IQR of 2 skittles. Finally, the males’ data appears to be more symmetrical, while the females’ data is clearly right skewed. Both are unimodal when we look at histograms of the data. 6

7. 2 sample t test on male vs. female average skittles • Conditions • Statement • Hypotheses: Ho: µF = µM Ha: µF ≠ µM • Test statistic • P-value • Conclusion • If reject, complete a confidence interval

8. SUMMARY STATS NEEDED FOR EACH VALUE OF THE VARIABLE Quantitative Data by Candy Consumption 8

9. (frequently, sometimes, or never) We chose to compare Candy Consumption to the data results to see if there was an association between people who ate Candy Frequently, Sometimes, or Never with the number of Skittles they correctly identified. • Our categorical variable that worked best with the data was if the subject ate candy on a regular basis (Frequently). The Frequently variable was more consistent overall than the Sometimes and Never variables • Frequently had a median at 3.5 skittles, which was the highest. Next was Sometimes with a median of 2 skittles, And never was the lowest with a median of ½ skittles. • The Sometimes variable had the largest range from (0,6) making it the most inconsistent, where as, the Never variable had a range from (0,1) and the Frequently variable had a range of (2, 5). The IQR of Frequently was ………… • The shape of frequently was …….. Quantitative Data by Candy Consumption 9

10. NOTE: • If your categorical variable only has 2 values, instead of doing a Chi-Square GOF test, complete a 1 prop Z test to see if the % of one of the values is = 50% X2 GOF test on type of Candy Consumption NEVER SOMETIMES FREQ OBS 10 28 30 EXP (uniform) • Conditions • Statement • Hypotheses • Test Statistic • P-Value • Conclusion

11. Simple Two-Way Table of Categorical Data 11

12. Male: 52.5 % Female: 47.5% Marginal Distribution for Gender 12

13. Chocolate: 50% Sweet: 50% Marginal Distribution for Candy Preference 13

14. List the % in each category: Of the females: C = 57.89% S = 42.11% Of the males: C = 42.86% S = 57.14% SEGMENTED BAR GRAPH

15. (made in Excel & copied in) • X2 Test for Association • Observed Table: Expected Table • Conditions • Statement • Hypotheses • Test Statistic • P-value • Conclusion

17. Sources of Error and Bias 17

18. Make a conclusion about each of the tests/intervals you did • Make a conclusion about each graph you made Conclusion 18